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Towards practical page coloring-based multicore cache management
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Source
European Conference on Computer Systems archive
Proceedings of the 4th ACM European conference on Computer systems table of contents
Nuremberg, Germany
SESSION: OS mechanisms table of contents
Pages 89-102  
Year of Publication: 2009
ISBN:978-1-60558-482-9
Authors
Xiao Zhang  University of Rochester, Rochester, NY, USA
Sandhya Dwarkadas  University of Rochester, Rochester, NY, USA
Kai Shen  University of Rochester, Rochester, NY, USA
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Modern multi-core processors present new resource management challenges due to the subtle interactions of simultaneously executing processes sharing on-chip resources (particularly the L2 cache). Recent research demonstrates that the operating system may use the page coloring mechanism to control cache partitioning, and consequently to achieve fair and efficient cache utilization. However, page coloring places additional constraints on memory space allocation, which may conflict with application memory needs. Further, adaptive adjustments of cache partitioning policies in a multi-programmed execution environment may incur substantial overhead for page recoloring (or copying). This paper proposes a hot-page coloring approach enforcing coloring on only a small set of frequently accessed (or hot) pages for each process. The cost of identifying hot pages online is reduced by leveraging the knowledge of spatial locality during a page table scan of access bits. Our results demonstrate that hot page identification and selective coloring can significantly alleviate the coloring-induced adverse effects in practice. However, we also reach the somewhat negative conclusion that without additional hardware support, adaptive page coloring is only beneficial when recoloring is performed infrequently (meaning long scheduling time quanta in multi-programmed executions).


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Xiao Zhang: colleagues
Sandhya Dwarkadas: colleagues
Kai Shen: colleagues